A generalized approach to randomised response for quantitative variables

The methodology of randomised response (RR) has advanced considerably in recent years. Nevertheless, most research in this area has addressed the estimation of qualitative variables, and relatively little attention has been paid to the study of quantitative ones. Furthermore, most studies concern only simple random sampling. In this paper, we present a new model of RR aimed at determining a total that is valid for any sampling design. This general procedure includes several important RR techniques that constitute particular cases. We propose an unbiased estimator, with an analytic expression for its variance. Confidence intervals are also obtained for the parameter, applying analytical formulae such as those based on resampling technologies. A simulation study illustrates the behaviour of the estimator using diverse randomisation devices and for different scrambling distributions. To illustrate the advantages of this method, we obtained a stratified clustered sample of university students, who were questioned to determine the frequency with which they cheated in exams. Their responses to these questions were obtained via an RR technique, and also using a direct questionnaire. We conclude that estimates based on anonymous questionnaires may result in severe underestimation.

[1]  J. Strickler,et al.  Measuring Induced Abortion in Mexico , 2004 .

[2]  D. Horvitz,et al.  Application of the Randomized Response Technique in Obtaining Quantitative Data , 1971 .

[3]  Ben Jann,et al.  Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT) , 2011 .

[4]  Jong-Min Kim,et al.  A pseudo-empirical log-likelihood estimator using scrambled responses , 2011 .

[5]  Jean-Paul Fox,et al.  A Mixed Effects Randomized Item Response Model , 2008 .

[6]  Kuo-Chung Huang,et al.  Estimation for sensitive characteristics using optional randomized response technique , 2008 .

[7]  Peter G. M. van der Heijden,et al.  The logistic regression model with response variables subject to randomized response , 2007, Comput. Stat. Data Anal..

[8]  Carlos N. Bouza,et al.  A review of randomized responses procedures: the qualitative variable case , 2010 .

[9]  Lakhbir S. Hayre,et al.  Scrambled randomized response methods for obtaining sensitive quantitative data , 1983 .

[10]  Giancarlo Diana,et al.  New scrambled response models for estimating the mean of a sensitive quantitative character , 2010 .

[11]  Raghunath Arnab Optimum Sampling Strategies under Randomized Response Surveys , 2002 .

[12]  Peter G. M. van der Heijden,et al.  Meta-Analysis of Randomized Response Research , 2005 .

[13]  Horng-Jinh Chang,et al.  On estimating the proportion of a qualitative sensitive character using randomized response sampling , 2005 .

[14]  J. Hox,et al.  A Comparison of Randomized Response, Computer-Assisted Self-Interview, and Face-to-Face Direct Questioning , 2000 .

[15]  Lynne Stokes,et al.  Introduction to Variance Estimation (2nd ed.) , 2008 .

[16]  Ranhunath Arnab,et al.  Nonnegative variance estimation in randomized response surveys , 1994 .

[17]  Arijit Chaudhuri,et al.  Randomized Response and Indirect Questioning Techniques in Surveys , 2010 .

[18]  Oluseun Odumade,et al.  An Alternative to the Bar-Lev, Bobovitch, and Boukai Randomized Response Model , 2010 .

[19]  C. Mitchell Dayton,et al.  Improved estimation of academic cheating behavior using the randomized response technique , 1987 .

[20]  Ivar Krumpal Determinants of social desirability bias in sensitive surveys: a literature review , 2013 .

[21]  Sarjinder Singh,et al.  A new randomized response model , 2006 .

[22]  Raghunath Arnab,et al.  Optional Randomized Response Techniques for Complex Survey Designs , 2004 .

[23]  Benzion Boukai,et al.  A note on randomized response models for quantitative data , 2004 .

[24]  Jean-Paul Fox,et al.  Using Item Response Theory to Obtain Individual Information From Randomized Response Data: An Application Using Cheating Data , 2008 .

[25]  Kenneth H. Pollock,et al.  A Comparison of Three Randomized Response Models for Quantitative Data , 1976 .

[26]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[27]  Amitava Saha,et al.  A simple randomized response technique in complex surveys , 2007 .

[28]  Sven A. Eriksson A New Model for Randomized Response , 1973 .

[29]  Carlos N. Bouza Ranked set sampling and randomized response procedures for estimating the mean of a sensitive quantitative character , 2008 .

[30]  Sarjinder Singh,et al.  Forced quantitative randomized response model: a new device , 2007 .

[31]  K. Wolter Introduction to Variance Estimation , 1985 .

[32]  Giancarlo Diana,et al.  A calibration-based approach to sensitive data: a simulation study , 2012 .